Movement rehabilitation is slowly transitioning towards an evidence and theory driven mode of operating where successful treatments are guided by an understanding of how the brain and the body work. As part of this transition, the development of models is very important. In the space of models, Bayesian and optimal control models have been particularly influential over the last decade or two. Models about statistically efficient processing in the brain (Bayesian brain) have been influential and assume that the brain is hardwired for solving statistical problems. Other, generally less popular, models assume that the brain just learns to act in a statistically efficient way from trial and error. For the interpretation of behavioral results this difference is crucial. An answer to this question promise to inform theories, electrophysiology and rehabilitation. Here we propose to use experiments with human subjects and model building to quantify the role of learning for the processing of uncertainty. We compare over trained tasks with novel tasks to ask if humans are hardwired for statistically efficient integration. We compare adults with children to ask if the appropriate experience solving these tasks is necessary. We perform generalization experiments to analyze the neural representation of uncertainty. And lastly, we build models of general learning systems for comparison with existing Bayesian models and test them on large databases. Across the experiments we will juxtapose two-alternative-forced-choice paradigms that quantify uncertainty with sensorimotor integration experiments that allow estimating its effect on behavior. The planned experiments will provide a nuanced answer to the question of how human behavior becomes so efficient in a statistical sense for important tasks. The three aims to be investigated in this project are: Aim 1: Determine if the brain needs to learn how to integrate uncertain sensorimotor information. We will ask if learning is necessary to allow humans to efficiently combine multiple pieces of uncertain information. While many current theories implicitly assume that efficient integration requires no learning our preliminary data suggests otherwise. Studying uncertainty is important as it is ubiquitous and affects learning. Aim 2: Determine if the representation of probability distributions and thus generalization is hardwired. We will ask if th generalization of uncertainty is hardwired or can be learned. This is important because generalization across tasks is one of the prime objectives of movement rehabilitation. Aim 3: Construct and test new models that learn how to integrate uncertain information. We will construct models, based on the idea of deep learning, that can explain statistically efficient processing in the brain without requiring it to be built in. The models will be tested against hardwired Bayesian models and a large database of behaviors. By building strong models that make accurate predictions about current and potential behavior would allow for more efficient and targeted movement rehabilitation.